A Subpattern Level Inspection System for Printed Circuit Boards

Abstract

The existing inspection systems are algorithmically slow and suffer from many drawbacks in meeting the inspection requirements of printed circuit board fabrication. The system presented in this paper can handle all of the defects simultaneously with the same approach and is significantly faster compared to the existing approaches. The system consists of three major phases: the first step is the segmentation of the golden PCB image into basic subpatterns, the second step is the learning phase, and the third and final step is the verification/inspection phase. The system presented here introduces the application of neural networks and fuzzy logic in printed circuit board inspection. The method is highly parallel and works at the subpattern level. Experimental results which demonstrate the effectiveness of the proposed algorithms are given. © 1993 Academic Press.

Department(s)

Computer Science

Keywords and Phrases

Associative memories; Design-rule checking; Feature extraction; Fuzzy logic; Image segmentation; Neural networks; Printed circuit board inspection; Reference comparison

International Standard Serial Number (ISSN)

1077-3142

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

01 Jan 1998

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